| A universal approximation theorem of deep neural networks for expressing probability distributions Y Lu, J Lu Advances in neural information processing systems 33, 3094-3105, 2020 | 290 | 2020 |
| A priori generalization analysis of the deep Ritz method for solving high dimensional elliptic partial differential equations Y Lu, J Lu, M Wang Conference on learning theory, 3196-3241, 2021 | 153* | 2021 |
| A mean field analysis of deep resnet and beyond: Towards provably optimization via overparameterization from depth Y Lu, C Ma, Y Lu, J Lu, L Ying International Conference on Machine Learning, 6426-6436, 2020 | 116 | 2020 |
| Scaling limit of the Stein variational gradient descent: The mean field regime J Lu, Y Lu, J Nolen SIAM Journal on Mathematical Analysis 51 (2), 648-671, 2019 | 111 | 2019 |
| A Bayesian level set method for geometric inverse problems MA Iglesias, Y Lu, AM Stuart Interfaces and free boundaries 18 (2), 181-217, 2016 | 104 | 2016 |
| Accelerating Langevin sampling with birth-death Y Lu, J Lu, J Nolen arXiv preprint arXiv:1905.09863, 2019 | 72 | 2019 |
| On the representation of solutions to elliptic pdes in barron spaces Z Chen, J Lu, Y Lu Advances in neural information processing systems 34, 6454-6465, 2021 | 51 | 2021 |
| Transfer learning enhanced deeponet for long-time prediction of evolution equations W Xu, Y Lu, L Wang Proceedings of the AAAI Conference on Artificial Intelligence 37 (9), 10629 …, 2023 | 49 | 2023 |
| Quantitative propagation of chaos in a bimolecular chemical reaction-diffusion model TS Lim, Y Lu, JH Nolen SIAM Journal on Mathematical Analysis 52 (2), 2098-2133, 2020 | 48 | 2020 |
| A priori generalization error analysis of two-layer neural networks for solving high dimensional Schrödinger eigenvalue problems J Lu, Y Lu Communications of the American Mathematical Society 2 (1), 1-21, 2022 | 44 | 2022 |
| Gaussian Approximations for Probability Measures on Y Lu, A Stuart, H Weber SIAM/ASA Journal on Uncertainty Quantification 5 (1), 1136-1165, 2017 | 44 | 2017 |
| Birth–death dynamics for sampling: global convergence, approximations and their asymptotics Y Lu, D Slepčev, L Wang Nonlinearity 36 (11), 5731, 2023 | 40 | 2023 |
| Geometric ergodicity of Langevin dynamics with Coulomb interactions Y Lu, JC Mattingly Nonlinearity 33 (2), 675, 2019 | 38 | 2019 |
| The factorization method for inverse elastic scattering from periodic structures G Hu, Y Lu, B Zhang Inverse Problems 29 (11), 115005, 2013 | 37 | 2013 |
| Uniform-in-time weak error analysis for stochastic gradient descent algorithms via diffusion approximation Y Feng, T Gao, L Li, JG Liu, Y Lu arXiv preprint arXiv:1902.00635, 2019 | 34 | 2019 |
| Two-scale gradient descent ascent dynamics finds mixed Nash equilibria of continuous games: A mean-field perspective Y Lu International Conference on Machine Learning, 22790-22811, 2023 | 33 | 2023 |
| On the convergence of Sobolev gradient flow for the Gross–Pitaevskii eigenvalue problem Z Chen, J Lu, Y Lu, X Zhang SIAM Journal on Numerical Analysis 62 (2), 667-691, 2024 | 31 | 2024 |
| A Regularity Theory for Static Schrödinger Equations on d in Spectral Barron Spaces Z Chen, J Lu, Y Lu, S Zhou SIAM Journal on Mathematical Analysis 55 (1), 557-570, 2023 | 31 | 2023 |
| Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian probability distributions F Cole, Y Lu arXiv preprint arXiv:2402.08082, 2024 | 30 | 2024 |
| Solving multiscale steady radiative transfer equation using neural networks with uniform stability Y Lu, L Wang, W Xu Research in the Mathematical Sciences 9 (3), 45, 2022 | 28 | 2022 |
Jianfeng Lu 鲁剑锋Department of Mathematics, Department of Chemistry and Department of Physics, Duke UniversityVerified email at math.duke.edu
